# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
                                                             img.jpg        # image
                                                             vid.mp4        # video
                                                             path/          # directory
                                                             path/*.jpg     # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # OpenVINO
                                         yolov5s.engine             # TensorRT
                                         yolov5s.mlmodel            # CoreML (macOS-only)
                                         yolov5s_saved_model        # TensorFlow SavedModel
                                         yolov5s.pb                 # TensorFlow GraphDef
                                         yolov5s.tflite             # TensorFlow Lite
                                         yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
"""
from utils.augmentations import letterbox
import numpy as np



import argparse
import os
import sys
from pathlib import Path

import torch
import torch.backends.cudnn as cudnn

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync

def getwhc(pred,img_w,img_h):
    if len(pred[0].tolist())>0:
        frame=pred[0].tolist()[0]
        return frame
    else:
        return [0,0,0,0]


class LoadImages:
    # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
    def __init__(self, img, img_size=640, stride=32, auto=True):
        # p = str(Path(path).resolve())  # os-agnostic absolute path
        # if '*' in p:
        #     files = sorted(glob.glob(p, recursive=True))  # glob
        # elif os.path.isdir(p):
        #     files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
        # elif os.path.isfile(p):
        #     files = [p]  # files
        # else:
        #     raise Exception(f'ERROR: {p} does not exist')

        # images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
        # videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
        # ni, nv = len(images), len(videos)
        ni = 1
        nv = 0
        self.img_size = img_size
        self.stride = stride
        self.files = [img]
        self.img = img
        self.nf = ni + nv  # number of files
        self.video_flag = [False] * ni + [True] * nv
        self.mode = 'image'
        self.auto = auto
        # if any(videos):
        #     self.new_video(videos[0])  # new video
        # else:
        self.cap = None
        assert self.nf > 0, f'No images or videos found in {p}. ' \
                            f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count == self.nf:
            raise StopIteration
        path = self.files[self.count]

        if self.video_flag[self.count]:
            # Read video
            self.mode = 'video'
            ret_val, img0 = self.cap.read()
            while not ret_val:
                self.count += 1
                self.cap.release()
                if self.count == self.nf:  # last video
                    raise StopIteration
                else:
                    path = self.files[self.count]
                    self.new_video(path)
                    ret_val, img0 = self.cap.read()

            self.frame += 1
            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '

        else:
            # Read image
            self.count += 1
            img0 = self.img  # BGR
            assert img0 is not None, f'Image Not Found {path}'
            s = f'image {self.count}/{self.nf} {path}: '

        # Padded resize
        img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0]

        # Convert
        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
        img = np.ascontiguousarray(img)

        return path, img, img0, self.cap, s

    def new_video(self, path):
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))

    def __len__(self):
        return self.nf  # number of files


@torch.no_grad()
def run(
        weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    img = cv2.imread("data/images/1.jpg")

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(img, img_size=imgsz, stride=stride, auto=pt)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(img, img_size=imgsz, stride=stride, auto=pt)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    for path, im, im0s, vid_cap, s in dataset:
        img_w=len(im0s[0])
        img_h=len(im0s)
        t1 = time_sync()
        im = torch.from_numpy(im).to(device)
        im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
        im /= 255  # 0 - 255 to 0.0 - 1.0
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        t2 = time_sync()
        dt[0] += t2 - t1

        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3


        # for i, det in enumerate(pred):  # detections per image
        #     if webcam:  # batch_size >= 1
        #         p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
        #     else:
        #         p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

        #     p = Path(p)  # to Path
        #     save_path = str(save_dir / p.name)  # img.jpg
        #     txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
        #     s += '%gx%g ' % img.shape[2:]  # print string
        #     gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
        #     if len(det):
        #         # Rescale boxes from img_size to im0 size
        #         det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

        #         # Print results
        #         for c in det[:, -1].unique():
        #             n = (det[:, -1] == c).sum()  # detections per class
        #             s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

        #         # Write results
        #         for *xyxy, conf, cls in reversed(det):
        #             if save_txt:  # Write to file
        #                 xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
        #                 line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
        #                 with open(txt_path + '.txt', 'a') as f:
        #                     f.write(('%g ' * len(line)).rstrip() % line + '\n')

        #             if save_img or view_img:  # Add bbox to image
        #                 label = f'{names[int(cls)]} {conf:.2f}'
        #                 plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

        #     # Print time (inference + NMS)
        #     t2 = time_synchronized()
        #     print(f'{s}Done. ({t2 - t1:.3f}s)')

        # # Stream results
        # if view_img:
        #     pass
        #     #cv2.imshow(str(p), im0)
        #     #cv2.waitKey(1)  # 1 millisecond

        # # Save results (image with detections)
        # if save_img:
        #     if dataset.mode == 'image':
        #         cv2.imshow('src2', im0) #gai
        #         cv2.waitKey(0)
        #     else:  # 'video' or 'stream'
        #         if vid_path != save_path:  # new video
        #             vid_path = save_path
        #             if isinstance(vid_writer, cv2.VideoWriter):
        #                 vid_writer.release()  # release previous video writer
        #             if vid_cap:  # video
        #                 fps = vid_cap.get(cv2.CAP_PROP_FPS)
        #                 w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        #                 h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        #             else:  # stream
        #                 fps, w, h = 30, im0.shape[1], im0.shape[0]
        #                 save_path += '.mp4'
        #             vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
        #         vid_writer.write(im0)

        y=getwhc(pred, img_w, img_h)
        print(y)
        # for i, det in enumerate(pred):  # per image
        #     seen += 1
        #     if webcam:  # batch_size >= 1
        #         p, im0, frame = path[i], im0s[i].copy(), dataset.count
        #         s += f'{i}: '
        #     else:
        #         p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
if __name__ == "__main__":
    check_requirements(exclude=('tensorboard', 'thop'))
    run()







# from utils.torch_utils import select_device
# from models.common import DetectMultiBackend
# from utils.general import check_img_size,cv2
# from utils.plots import Annotator


# def run(
#         im0,
#         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
#         weights= 'yolov5s.pt',  # model.pt path(s)
#         dnn=False,  # use OpenCV DNN for ONNX inference
#         data= 'data/coco128.yaml',  # dataset.yaml path
#         half=False,  # use FP16 half-precision inference
#         imgsz=(640, 640),  # inference size (height, width)
#         line_thickness=3,  # bounding box thickness (pixels)
# ):


#     device = select_device(device)
#     model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
#     stride, names, pt = model.stride, model.names, model.pt
#     imgsz = check_img_size(imgsz, s=stride)  # check image size

    

#     annotator = Annotator(im0, line_width=line_thickness, example=str(names))
#     im0 = annotator.result()
#     cv2.imshow('src2', im0) #gai
#     cv2.waitKey(0)
# if __name__ == "__main__":
#     # src = cv2.imread("data/images/1.jpg")
#     cap = cv2.VideoCapture(-1)
#     ret, frame = cap.read()
#     run(frame)

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.

Usage - sources:
    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
                                                             img.jpg        # image
                                                             vid.mp4        # video
                                                             path/          # directory
                                                             path/*.jpg     # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # OpenVINO
                                         yolov5s.engine             # TensorRT
                                         yolov5s.mlmodel            # CoreML (macOS-only)
                                         yolov5s_saved_model        # TensorFlow SavedModel
                                         yolov5s.pb                 # TensorFlow GraphDef
                                         yolov5s.tflite             # TensorFlow Lite
                                         yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
"""

# import argparse
# import os
# import sys
# from pathlib import Path

# import torch
# import torch.backends.cudnn as cudnn

# FILE = Path(__file__).resolve()
# ROOT = FILE.parents[0]  # YOLOv5 root directory
# if str(ROOT) not in sys.path:
#     sys.path.append(str(ROOT))  # add ROOT to PATH
# ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

# from models.common import DetectMultiBackend
# from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
# from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
#                            increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
# from utils.plots import Annotator, colors, save_one_box
# from utils.torch_utils import select_device, time_sync


# @torch.no_grad()
# def run(
#         weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
#         source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
#         data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
#         imgsz=(640, 640),  # inference size (height, width)
#         conf_thres=0.25,  # confidence threshold
#         iou_thres=0.45,  # NMS IOU threshold
#         max_det=1000,  # maximum detections per image
#         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
#         view_img=False,  # show results
#         save_txt=False,  # save results to *.txt
#         save_conf=False,  # save confidences in --save-txt labels
#         save_crop=False,  # save cropped prediction boxes
#         nosave=False,  # do not save images/videos
#         classes=None,  # filter by class: --class 0, or --class 0 2 3
#         agnostic_nms=False,  # class-agnostic NMS
#         augment=False,  # augmented inference
#         visualize=False,  # visualize features
#         update=False,  # update all models
#         project=ROOT / 'runs/detect',  # save results to project/name
#         name='exp',  # save results to project/name
#         exist_ok=False,  # existing project/name ok, do not increment
#         line_thickness=3,  # bounding box thickness (pixels)
#         hide_labels=False,  # hide labels
#         hide_conf=False,  # hide confidences
#         half=False,  # use FP16 half-precision inference
#         dnn=False,  # use OpenCV DNN for ONNX inference
# ):
#     source = str(source)
#     save_img = not nosave and not source.endswith('.txt')  # save inference images
#     is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
#     is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
#     webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
#     if is_url and is_file:
#         source = check_file(source)  # download

#     # Directories
#     save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
#     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

#     # Load model
#     device = select_device(device)
#     model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
#     stride, names, pt = model.stride, model.names, model.pt
#     imgsz = check_img_size(imgsz, s=stride)  # check image size

#     # Dataloader
#     if webcam:
#         view_img = check_imshow()
#         cudnn.benchmark = True  # set True to speed up constant image size inference
#         dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
#         bs = len(dataset)  # batch_size
#     else:
#         dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
#         bs = 1  # batch_size
#     vid_path, vid_writer = [None] * bs, [None] * bs

#     # Run inference
#     model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
#     dt, seen = [0.0, 0.0, 0.0], 0
#     for path, im, im0s, vid_cap, s in dataset:
#         t1 = time_sync()
#         im = torch.from_numpy(im).to(device)
#         im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
#         im /= 255  # 0 - 255 to 0.0 - 1.0
#         if len(im.shape) == 3:
#             im = im[None]  # expand for batch dim
#         t2 = time_sync()
#         dt[0] += t2 - t1

#         # Inference
#         visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
#         pred = model(im, augment=augment, visualize=visualize)
#         t3 = time_sync()
#         dt[1] += t3 - t2

#         # NMS
#         pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
#         dt[2] += time_sync() - t3

#         # Second-stage classifier (optional)
#         # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

#         # Process predictions
#         for i, det in enumerate(pred):  # per image
#             seen += 1
#             if webcam:  # batch_size >= 1
#                 p, im0, frame = path[i], im0s[i].copy(), dataset.count
#                 s += f'{i}: '
#             else:
#                 p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

#             p = Path(p)  # to Path
#             save_path = str(save_dir / p.name)  # im.jpg
#             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
#             s += '%gx%g ' % im.shape[2:]  # print string
#             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
#             imc = im0.copy() if save_crop else im0  # for save_crop
#             annotator = Annotator(im0, line_width=line_thickness, example=str(names))
#             if len(det):
#                 # Rescale boxes from img_size to im0 size
#                 det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

#                 # Print results
#                 for c in det[:, -1].unique():
#                     n = (det[:, -1] == c).sum()  # detections per class
#                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

#                 # Write results
#                 for *xyxy, conf, cls in reversed(det):
#                     if save_txt:  # Write to file
#                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
#                         line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
#                         with open(txt_path + '.txt', 'a') as f:
#                             f.write(('%g ' * len(line)).rstrip() % line + '\n')

#                     if save_img or save_crop or view_img:  # Add bbox to image
#                         c = int(cls)  # integer class
#                         label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
#                         annotator.box_label(xyxy, label, color=colors(c, True))
#                         if save_crop:
#                             save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

#             # Stream results
#             im0 = annotator.result()
#             if view_img:
#                 cv2.imshow(str(p), im0)
#                 cv2.waitKey(0)
#                 cv2.destoryAllWindows()

#             # Save results (image with detections)
#             if save_img:
#                 if dataset.mode == 'image':
#                     cv2.imwrite(save_path, im0)
#                 else:  # 'video' or 'stream'
#                     if vid_path[i] != save_path:  # new video
#                         vid_path[i] = save_path
#                         if isinstance(vid_writer[i], cv2.VideoWriter):
#                             vid_writer[i].release()  # release previous video writer
#                         if vid_cap:  # video
#                             fps = vid_cap.get(cv2.CAP_PROP_FPS)
#                             w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
#                             h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#                         else:  # stream
#                             fps, w, h = 30, im0.shape[1], im0.shape[0]
#                         save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
#                         vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
#                     vid_writer[i].write(im0)

#         # Print time (inference-only)
#         LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

#     # Print results
#     t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
#     LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
#     if save_txt or save_img:
#         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
#         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
#     if update:
#         strip_optimizer(weights)  # update model (to fix SourceChangeWarning)


# def parse_opt():
#     parser = argparse.ArgumentParser()
#     parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
#     parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
#     parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
#     parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
#     parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
#     parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
#     parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
#     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
#     parser.add_argument('--view-img', action='store_true', help='show results')
#     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
#     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
#     parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
#     parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
#     parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
#     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
#     parser.add_argument('--augment', action='store_true', help='augmented inference')
#     parser.add_argument('--visualize', action='store_true', help='visualize features')
#     parser.add_argument('--update', action='store_true', help='update all models')
#     parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
#     parser.add_argument('--name', default='exp', help='save results to project/name')
#     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
#     parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
#     parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
#     parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
#     parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
#     parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
#     opt = parser.parse_args()
#     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
#     print_args(vars(opt))
#     return opt


# def main(opt):
#     check_requirements(exclude=('tensorboard', 'thop'))
#     run(**vars(opt))


# if __name__ == "__main__":
#     opt = parse_opt()
#     main(opt)
